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Attrition Predictor Analysis







Project By:


Heindel Adu, Stephen Johnson, Ross Fu, Anthony Yeung


Classification:
CONFIDENTIAL REPORT!

Int Data Viz

Row

Attrition Count

Attrition

Number of Employee Data Reviewed

1470

Monthly Income

Yes on Attrition

237

No on Attrition

1233

Row

Plotly Bar Plot

Plotly Box Plot of Job Role & Income

Scatter Plots

Row

Monthly Income v Age

Monthly Income v Time in C. Role

Monthly Income v Time At Company

Row

Comparison of Monthly Income with other variables.


  1. Age


  2. Time in Current Role


  3. Time with Company

Data Table

Pivot Table - Mixed

R Forest

Row

RF Parameters 


Call:
 randomForest(formula = AttritionN ~ ., data = training, ntree = 300,      mtry = 8, importance = TRUE, proximity = TRUE) 
               Type of random forest: classification
                     Number of trees: 300
No. of variables tried at each split: 8

        OOB estimate of  error rate: 13.96%
Confusion matrix:
    0  1 class.error
0 880 13  0.01455767
1 135 32  0.80838323

RF Attributes 

$names
 [1] "call"            "type"            "predicted"      
 [4] "err.rate"        "confusion"       "votes"          
 [7] "oob.times"       "classes"         "importance"     
[10] "importanceSD"    "localImportance" "proximity"      
[13] "ntree"           "mtry"            "forest"         
[16] "y"               "test"            "inbag"          
[19] "terms"          

$class
[1] "randomForest.formula" "randomForest"

Row

Error Rate

# Nodes for the Trees

Matrix

Row

Training C-Matrix 

Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 893   0
         1   0 167
                                     
               Accuracy : 1          
                 95% CI : (0.9965, 1)
    No Information Rate : 0.8425     
    P-Value [Acc > NIR] : < 2.2e-16  
                                     
                  Kappa : 1          
                                     
 Mcnemar's Test P-Value : NA         
                                     
            Sensitivity : 1.0000     
            Specificity : 1.0000     
         Pos Pred Value : 1.0000     
         Neg Pred Value : 1.0000     
             Prevalence : 0.8425     
         Detection Rate : 0.8425     
   Detection Prevalence : 0.8425     
      Balanced Accuracy : 1.0000     
                                     
       'Positive' Class : 0

Testing C-Matrix 

Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 339  58
         1   1  12
                                          
               Accuracy : 0.8561          
                 95% CI : (0.8183, 0.8886)
    No Information Rate : 0.8293          
    P-Value [Acc > NIR] : 0.08188         
                                          
                  Kappa : 0.249           
                                          
 Mcnemar's Test P-Value : 3.086e-13       
                                          
            Sensitivity : 0.9971          
            Specificity : 0.1714          
         Pos Pred Value : 0.8539          
         Neg Pred Value : 0.9231          
             Prevalence : 0.8293          
         Detection Rate : 0.8268          
   Detection Prevalence : 0.9683          
      Balanced Accuracy : 0.5842          
                                          
       'Positive' Class : 0

RF Plots

Row

Variable Importance - Top 10

Partial Dependence

Multi-dim Scaling Plot of Proximity

Row

Single Tree 

    left daughter right daughter                split var split point
1               2              3                 JobLevel         1.5
2               4              5                OverTimeN         0.5
3               6              7           YearsAtCompany         3.5
4               8              9                JobRole_N         1.5
5              10             11         DistanceFromHome         2.5
6              12             13          WorkLifeBalance         2.5
7              14             15         StockOptionLevel         0.5
8              16             17  EnvironmentSatisfaction         3.5
9              18             19        TotalWorkingYears         2.5
10             20             21  EnvironmentSatisfaction         1.5
11             22             23 RelationshipSatisfaction         2.5
12             24             25            MonthlyIncome      4500.5
13             26             27              MonthlyRate     21630.0
14             28             29         DistanceFromHome        24.5
15             30             31                      Age        46.5
16             32             33        PercentSalaryHike        16.0
17             34             35          EducationFieldN         5.0
18             36             37                JobRole_N         3.5
19             38             39     YearsWithCurrManager         1.5
20             40             41          JobSatisfaction         3.0
21             42             43     YearsWithCurrManager         0.5
22             44             45         DistanceFromHome         9.5
23             46             47        PerformanceRating         3.5
24             48             49              MonthlyRate     15694.5
25             50             51                DailyRate      1159.5
26             52             53          JobSatisfaction         3.5
27             54             55              DepartmentN         1.5
28             56             57              MonthlyRate     26770.5
29             58             59               HourlyRate        52.5
30             60             61              MonthlyRate      2574.5
31             62             63                      Age        48.0
32             64             65         DistanceFromHome        16.0
33             66             67       NumCompaniesWorked         5.5
34              0              0                     <NA>         0.0
35              0              0                     <NA>         0.0
36             68             69        PercentSalaryHike        19.5
37             70             71                DailyRate       355.5
38             72             73              MonthlyRate      9606.5
39             74             75         DistanceFromHome        28.5
40              0              0                     <NA>         0.0
41              0              0                     <NA>         0.0
42             76             77  EnvironmentSatisfaction         3.0
43             78             79               HourlyRate        93.0
44              0              0                     <NA>         0.0
45             80             81                Education         1.5
46             82             83       NumCompaniesWorked         4.5
47             84             85  EnvironmentSatisfaction         1.5
48              0              0                     <NA>         0.0
49              0              0                     <NA>         0.0
50             86             87        PercentSalaryHike        13.5
51             88             89  YearsSinceLastPromotion         1.0
52             90             91          EducationFieldN         2.5
53              0              0                     <NA>         0.0
54             92             93 RelationshipSatisfaction         2.5
55             94             95          JobSatisfaction         2.0
56             96             97              DepartmentN         1.5
57              0              0                     <NA>         0.0
58              0              0                     <NA>         0.0
59             98             99  YearsSinceLastPromotion        10.0
60            100            101            MonthlyIncome      7560.5
61            102            103  EnvironmentSatisfaction         3.5
62            104            105                DailyRate       434.0
63            106            107                Education         3.5
64              0              0                     <NA>         0.0
65              0              0                     <NA>         0.0
66            108            109  YearsSinceLastPromotion         1.0
67              0              0                     <NA>         0.0
68            110            111 RelationshipSatisfaction         2.0
69            112            113  EnvironmentSatisfaction         2.5
70              0              0                     <NA>         0.0
71              0              0                     <NA>         0.0
72              0              0                     <NA>         0.0
73            114            115                      Age        28.5
74            116            117                DailyRate       114.5
75              0              0                     <NA>         0.0
76              0              0                     <NA>         0.0
77              0              0                     <NA>         0.0
78            118            119              DepartmentN         1.5
79              0              0                     <NA>         0.0
80              0              0                     <NA>         0.0
81              0              0                     <NA>         0.0
82            120            121         StockOptionLevel         0.5
83            122            123           YearsAtCompany         1.5
84            124            125                      Age        38.0
85              0              0                     <NA>         0.0
86              0              0                     <NA>         0.0
87            126            127  EnvironmentSatisfaction         1.5
88              0              0                     <NA>         0.0
89              0              0                     <NA>         0.0
90            128            129                DailyRate       694.5
91            130            131 RelationshipSatisfaction         2.5
92            132            133                Education         3.5
93              0              0                     <NA>         0.0
94              0              0                     <NA>         0.0
95            134            135                  GenderN         0.5
96            136            137                      Age        57.0
97            138            139          WorkLifeBalance         2.5
98              0              0                     <NA>         0.0
99              0              0                     <NA>         0.0
100             0              0                     <NA>         0.0
101             0              0                     <NA>         0.0
102             0              0                     <NA>         0.0
103           140            141       YearsInCurrentRole         2.5
104             0              0                     <NA>         0.0
105             0              0                     <NA>         0.0
106           142            143           YearsAtCompany         5.5
107             0              0                     <NA>         0.0
108             0              0                     <NA>         0.0
109             0              0                     <NA>         0.0
110             0              0                     <NA>         0.0
111           144            145          BusinessTravelN         1.5
112             0              0                     <NA>         0.0
113             0              0                     <NA>         0.0
114             0              0                     <NA>         0.0
115           146            147               HourlyRate        83.5
116             0              0                     <NA>         0.0
117           148            149       NumCompaniesWorked         0.5
118           150            151          WorkLifeBalance         1.5
119           152            153         DistanceFromHome         1.5
120           154            155 RelationshipSatisfaction         3.5
121             0              0                     <NA>         0.0
122             0              0                     <NA>         0.0
123             0              0                     <NA>         0.0
124             0              0                     <NA>         0.0
125             0              0                     <NA>         0.0
126             0              0                     <NA>         0.0
127             0              0                     <NA>         0.0
128             0              0                     <NA>         0.0
129             0              0                     <NA>         0.0
130           156            157         StockOptionLevel         0.5
131             0              0                     <NA>         0.0
132           158            159                  GenderN         0.5
133             0              0                     <NA>         0.0
134             0              0                     <NA>         0.0
135             0              0                     <NA>         0.0
136           160            161    TrainingTimesLastYear         1.5
137           162            163  YearsSinceLastPromotion        12.0
138           164            165 RelationshipSatisfaction         3.5
139           166            167               HourlyRate        62.5
140           168            169                DailyRate       361.0
141             0              0                     <NA>         0.0
142           170            171           JobInvolvement         3.5
143           172            173        TotalWorkingYears         9.5
144           174            175                DailyRate       250.0
145             0              0                     <NA>         0.0
146           176            177       NumCompaniesWorked         4.5
147           178            179  YearsSinceLastPromotion         0.5
148           180            181          EducationFieldN         2.0
149           182            183        PercentSalaryHike        12.5
150           184            185 RelationshipSatisfaction         3.0
151             0              0                     <NA>         0.0
152             0              0                     <NA>         0.0
153             0              0                     <NA>         0.0
154           186            187        PercentSalaryHike        13.5
155           188            189                DailyRate       931.5
156           190            191                Education         3.0
157             0              0                     <NA>         0.0
158             0              0                     <NA>         0.0
159             0              0                     <NA>         0.0
160           192            193            MonthlyIncome     18858.5
161           194            195          JobSatisfaction         2.5
162             0              0                     <NA>         0.0
163             0              0                     <NA>         0.0
164           196            197         DistanceFromHome        11.0
165             0              0                     <NA>         0.0
166             0              0                     <NA>         0.0
167           198            199               HourlyRate        64.5
168             0              0                     <NA>         0.0
169             0              0                     <NA>         0.0
170           200            201                DailyRate       773.0
171             0              0                     <NA>         0.0
172             0              0                     <NA>         0.0
173             0              0                     <NA>         0.0
174             0              0                     <NA>         0.0
175             0              0                     <NA>         0.0
176             0              0                     <NA>         0.0
177             0              0                     <NA>         0.0
178             0              0                     <NA>         0.0
179             0              0                     <NA>         0.0
180             0              0                     <NA>         0.0
181             0              0                     <NA>         0.0
182           202            203               HourlyRate        36.0
183             0              0                     <NA>         0.0
184             0              0                     <NA>         0.0
185             0              0                     <NA>         0.0
186             0              0                     <NA>         0.0
187             0              0                     <NA>         0.0
188             0              0                     <NA>         0.0
189             0              0                     <NA>         0.0
190             0              0                     <NA>         0.0
191             0              0                     <NA>         0.0
192           204            205        TotalWorkingYears         9.5
193             0              0                     <NA>         0.0
194           206            207                Education         3.5
195             0              0                     <NA>         0.0
196           208            209              MonthlyRate     20889.5
197             0              0                     <NA>         0.0
198             0              0                     <NA>         0.0
199           210            211       YearsInCurrentRole         2.5
200             0              0                     <NA>         0.0
201             0              0                     <NA>         0.0
202             0              0                     <NA>         0.0
203           212            213               HourlyRate        82.5
204             0              0                     <NA>         0.0
205             0              0                     <NA>         0.0
206             0              0                     <NA>         0.0
207           214            215            MonthlyIncome      5074.5
208           216            217       YearsInCurrentRole         4.0
209             0              0                     <NA>         0.0
210           218            219        PercentSalaryHike        15.5
211             0              0                     <NA>         0.0
212             0              0                     <NA>         0.0
213           220            221  EnvironmentSatisfaction         3.0
214             0              0                     <NA>         0.0
215             0              0                     <NA>         0.0
216             0              0                     <NA>         0.0
217             0              0                     <NA>         0.0
218             0              0                     <NA>         0.0
219             0              0                     <NA>         0.0
220             0              0                     <NA>         0.0
221             0              0                     <NA>         0.0
    status prediction
1        1       <NA>
2        1       <NA>
3        1       <NA>
4        1       <NA>
5        1       <NA>
6        1       <NA>
7        1       <NA>
8        1       <NA>
9        1       <NA>
10       1       <NA>
11       1       <NA>
12       1       <NA>
13       1       <NA>
14       1       <NA>
15       1       <NA>
16       1       <NA>
17       1       <NA>
18       1       <NA>
19       1       <NA>
20       1       <NA>
21       1       <NA>
22       1       <NA>
23       1       <NA>
24       1       <NA>
25       1       <NA>
26       1       <NA>
27       1       <NA>
28       1       <NA>
29       1       <NA>
30       1       <NA>
31       1       <NA>
32       1       <NA>
33       1       <NA>
34      -1          0
35      -1          1
36       1       <NA>
37       1       <NA>
38       1       <NA>
39       1       <NA>
40      -1          1
41      -1          0
42       1       <NA>
43       1       <NA>
44      -1          1
45       1       <NA>
46       1       <NA>
47       1       <NA>
48      -1          1
49      -1          0
50       1       <NA>
51       1       <NA>
52       1       <NA>
53      -1          0
54       1       <NA>
55       1       <NA>
56       1       <NA>
57      -1          1
58      -1          0
59       1       <NA>
60       1       <NA>
61       1       <NA>
62       1       <NA>
63       1       <NA>
64      -1          1
65      -1          0
66       1       <NA>
67      -1          1
68       1       <NA>
69       1       <NA>
70      -1          0
71      -1          1
72      -1          0
73       1       <NA>
74       1       <NA>
75      -1          1
76      -1          0
77      -1          1
78       1       <NA>
79      -1          1
80      -1          0
81      -1          1
82       1       <NA>
83       1       <NA>
84       1       <NA>
85      -1          1
86      -1          0
87       1       <NA>
88      -1          0
89      -1          1
90       1       <NA>
91       1       <NA>
92       1       <NA>
93      -1          0
94      -1          1
95       1       <NA>
96       1       <NA>
97       1       <NA>
98      -1          1
99      -1          0
100     -1          0
101     -1          1
102     -1          0
103      1       <NA>
104     -1          0
105     -1          1
106      1       <NA>
107     -1          0
108     -1          1
109     -1          0
110     -1          1
111      1       <NA>
112     -1          1
113     -1          0
114     -1          1
115      1       <NA>
116     -1          1
117      1       <NA>
118      1       <NA>
119      1       <NA>
120      1       <NA>
121     -1          0
122     -1          0
123     -1          1
124     -1          1
125     -1          0
126     -1          1
127     -1          0
128     -1          0
129     -1          1
130      1       <NA>
131     -1          0
132      1       <NA>
133     -1          0
134     -1          1
135     -1          0
136      1       <NA>
137      1       <NA>
138      1       <NA>
139      1       <NA>
140      1       <NA>
141     -1          0
142      1       <NA>
143      1       <NA>
144      1       <NA>
145     -1          1
146      1       <NA>
147      1       <NA>
148      1       <NA>
149      1       <NA>
150      1       <NA>
151     -1          0
152     -1          1
153     -1          0
154      1       <NA>
155      1       <NA>
156      1       <NA>
157     -1          0
158     -1          0
159     -1          1
160      1       <NA>
161      1       <NA>
162     -1          0
163     -1          1
164      1       <NA>
165     -1          0
166     -1          0
167      1       <NA>
168     -1          1
169     -1          0
170      1       <NA>
171     -1          0
172     -1          1
173     -1          0
174     -1          1
175     -1          0
176     -1          0
177     -1          1
178     -1          0
179     -1          1
180     -1          1
181     -1          0
182      1       <NA>
183     -1          0
184     -1          0
185     -1          1
186     -1          1
187     -1          0
188     -1          0
189     -1          1
190     -1          1
191     -1          0
192      1       <NA>
193     -1          1
194      1       <NA>
195     -1          0
196      1       <NA>
197     -1          1
198     -1          1
199      1       <NA>
200     -1          1
201     -1          0
202     -1          1
203      1       <NA>
204     -1          1
205     -1          0
206     -1          0
207      1       <NA>
208      1       <NA>
209     -1          1
210      1       <NA>
211     -1          0
212     -1          0
213      1       <NA>
214     -1          1
215     -1          0
216     -1          1
217     -1          0
218     -1          0
219     -1          1
220     -1          1
221     -1          0

Tune Mtry 

0.004873494 0.05 
-0.05946155 0.05 

---
title: "Attrition Predictor Analysis"

output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    social: ["github","linkedin"]
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(ggvis)
library(broom)
```


```{r, message=FALSE}
#"data" has categorical data converted to numerical
# "data2" has a mixed data types
data <- read.csv("~/Documents/Projects/SMU/rProjects/CaseStudy_02/CaseStudy_02/NumData.csv")
data2 <- read.csv("~/Documents/Projects/SMU/rProjects/CaseStudy_02/CaseStudy_02/CaseStudy2-data.csv")
#View(data)
#str(data)
```

```{r echo=FALSE}
# embed all Rmd and csv files
xfun::embed_files(list.files('.', '[.](Rmd|csv)$'))
```

```{r}
mycolors <- c("blue", "#FFC125", "darkgreen", "darkorange", "darkblue")
```
About Report
========================================





Attrition Predictor Analysis














  
Project By:

  
Heindel Adu, Stephen Johnson, Ross Fu, Anthony Yeung 
 

  
Classification: 
  CONFIDENTIAL REPORT! 




Int Data Viz
=====================================
Row
-------------------------------------
### Attrition Count

```{r}
valueBox(paste("Attrition"),
         color = "darkblue")
#unique.(data$Attrition)

#data %>%
#  summarise(Attrition = n())
```

### Number of Employee Data Reviewed

```{r}

valueBox(length(data$Attrition),
         icon = "fa-users")
```

### **Monthly Income**
```{r}
gauge(round(min(data$MonthlyIncome),
            digits = 2),
            min = 0,
            max = 25000,
      label = "Min Monthly Income",
            gaugeSectors(success = c(15000, 25000),
                         warning = c(5000,15000),
                         danger = c(0, 5000),
                         colors = c("green", "yellow", "red")))
```

```{r}
gauge(round(mean(data$MonthlyIncome),
            digits = 2),
            min = 0,
            max = 25000,
       label = "Avg Monthly Income",
            gaugeSectors(success = c(15000, 25000),
                         warning = c(5000,15000),
                         danger = c(0, 5000),
                         colors = c("green", "yellow", "red")))

```

```{r}
gauge(round(max(data$MonthlyIncome),
            digits = 2),
            min = 0,
            max = 25000,
       label = "Max Monthly Income",
            gaugeSectors(success = c(15000, 25000),
                         warning = c(5000,15000),
                         danger = c(0, 5000),
                         colors = c("green", "yellow", "red")))
```

### Yes on Attrition

```{r, Yes}
valueBox(sum(data$Attrition == "Yes"),
         icon = 'fa-user-o')

```


### No on Attrition

```{r, No}
valueBox(sum(data$Attrition == "No"),
         icon = 'fa-user')
```

Row
-------------------------------------------------
### **Plotly Bar Plot **
```{r}
#summary(data2$Attrition)
#str(data2$Attrition)
#p0 <- ggplot2::data2 %>% sum(JobRole, Attrition) %>%
#  plot_ly(x = ~JobRole, y = ~n, color = ~Attrition)
p1 <- data2 %>%
  group_by(JobRole) %>%
  summarise(count = n()) %>%
  plot_ly(x = ~JobRole,
          y = ~count,
          color = 'blue',
          type = 'bar') %>%
  layout(xaxis = list(title = "Job Role"),
    yaxis = list(title = 'Count'))
p1
#names(data2)
```


### **Plotly Box Plot of Job Role & Income**
```{r}
data2 %>%
         group_by(JobRole) %>%
         plot_ly(y = ~JobRole, x = ~MonthlyIncome, type = "box", boxpoints = "outliers", color = ~JobRole, jitter = 0.3, pointpos = 0, orientation = "h")
```



Scatter Plots
=====================================
Row
-------------------------------------
### **Monthly Income v Age**
```{r}
j <- loess(MonthlyIncome ~ Age, data = data2)
p2 <- plot_ly(data = data2, x = ~Age,
          y = ~MonthlyIncome,
          color = as.factor(data2$Attrition),
          type = 'scatter', alpha = 0.9) %>%
  add_lines(y= ~fitted(loess(data2$MonthlyIncome ~ data2$Age)),
            line = list(color = 'rgba(7,164,181,1)'),
            name = "Loess Smoother") %>%
  add_ribbons(data = augment(j),
              ymin = ~.fitted - 1.96 * .se.fit,
              ymax = ~.fitted + 1.96 * .se.fit,
              line = list(color = 'rgba(7, 164, 181, 0.05)'),
              fillcolor = 'rgba(7, 164, 181, 0.2)',
              name = "Standard Error") %>%
    layout(xaxis = list(title = "Age"),
    yaxis = list(title = 'Monthly Income'))

p2
```

### **Monthly Income v  Time in C. Role**
```{r}
l <- loess(MonthlyIncome ~ YearsInCurrentRole, data = data2)
p3 <- plot_ly(data = data2, x = ~YearsInCurrentRole,
          y = ~MonthlyIncome,
          color = as.factor(data2$Attrition),
          type = 'scatter', alpha = 0.9) %>%
  add_lines(y= ~fitted(loess(data2$MonthlyIncome ~ data2$YearsInCurrentRole)),
            line = list(color = 'rgba(7,164,181,1)'),
            name = "Loess Smoother") %>%
  add_ribbons(data = augment(l),
              ymin = ~.fitted - 1.96 * .se.fit,
              ymax = ~.fitted + 1.96 * .se.fit,
              line = list(color = 'rgba(7, 164, 181, 0.05)'),
              fillcolor = 'rgba(7, 164, 181, 0.2)',
              name = "Standard Error") %>%
    layout(xaxis = list(title = "Years In Current Role"),
    yaxis = list(title = 'Monthly Income'))

p3
```

### **Monthly Income v Time At Company **
```{r}
k <- loess(MonthlyIncome ~ YearsAtCompany, data = data2)
p5 <- plot_ly(data = data2, x = ~YearsAtCompany,
          y = ~MonthlyIncome,
          color = as.factor(data2$Attrition),
          type = 'scatter', alpha = 0.9) %>%
  add_lines(y= ~fitted(loess(data2$MonthlyIncome ~ data2$YearsAtCompany)),
            line = list(color = 'rgba(7,164,181,1)'),
            name = "Loess Smoother") %>%
  add_ribbons(data = augment(k),
              ymin = ~.fitted - 1.96 * .se.fit,
              ymax = ~.fitted + 1.96 * .se.fit,
              line = list(color = 'rgba(7, 164, 181, 0.05)'),
              fillcolor = 'rgba(7, 164, 181, 0.2)',
              name = "Standard Error") %>%
    layout(xaxis = list(title = "Years At Company"),
    yaxis = list(title = 'Monthly Income'))

p5
```

Row
---------------------------------------------



 Comparison of Monthly Income with other variables.


     
  
  1.  Age 
    2.  
    
  
  2.  Time in Current Role 
    3.  
    
  
  3.  Time with Company 
    4.  
    




```{r}
### **Box Plot of Top State**
#data2 %>%
#         group_by(JobRole) %>%
#         ggvis(~JobRole, ~MonthlyIncome, fill = ~JobRole) %>%
#         layer_boxplots()
```


Data Table
========================================

```{r}
datatable(data2,
          caption = "Attrition Data",
          rownames = TRUE,
          filter = "top",
          options = list(pageLength = 25))
```

Pivot Table - Mixed
=========================================

```{r}
rpivotTable(data2,
            aggregatorName = "Count",
            cols= "Attrition",
            rows = "JobRole",
            rendererName = "Heatmap")
```



R Forest
========================================

```{r}
#View(data)
#names(data)
# Partition Data
#str(data$AttritionN)
data$AttritionN <- as.factor(data$AttritionN)
rfdata <-data[1:34]
#names(rfdata)
# Data Partition
set.seed(111)
ind <- sample(2, nrow(rfdata), replace = TRUE, prob = c(0.7, 0.3))
training <- rfdata[ind==1,]
testing <- rfdata[ind==2,]


# Random Forest
library(randomForest)
set.seed(222)
rf <- randomForest(AttritionN~., data=training,
                   ntree = 300,
                   mtry = 8,
                   importance = TRUE,
                   proximity = TRUE)
```

Row
----------------------------------------
### **RF Parameters **
```{r}
print(rf)
```

### **RF Attributes **
```{r}
attributes(rf)
```

Row
--------------------------------
### **Error Rate **
```{r}
# Error rate of Random Forest
plot(rf)
```

### **# Nodes for the Trees **
```{r}
# No. of nodes for the trees
hist(treesize(rf),
     main = "No. of Nodes for the Trees",
     col = "lightblue")
```

Matrix
======================================

Row 
--------------------------------------

### **Training C-Matrix **
```{r}
# Prediction & Confusion Matrix - training data
library(caret)
p9 <- predict(rf, training)
confusionMatrix(p9, training$AttritionN)
```

### **Testing C-Matrix **
```{r}
# # Prediction & Confusion Matrix - testing data
p10 <- predict(rf, testing)
confusionMatrix(p10, testing$AttritionN)
```



RF Plots
========================================
Row
--------------------------------
### **Variable Importance - Top 10 **
```{r}
# Variable Importance
varImpPlot(rf,
           sort = T,
           n.var = 10,
           main = "Top 10 - Variable Importance")
#importance(rf)

```

### **Partial Dependence **
```{r}
# Partial Dependence Plot
partialPlot(rf, training, OverTimeN, "1")
```

### **Multi-dim Scaling Plot of Proximity**
```{r}
# Multi-dimensional Scaling Plot of Proximity Matrix
MDSplot(rf, training$AttritionN)

```

Row
---------------------------------------------
### **Single Tree **
```{r}
# Extract Single Tree
getTree(rf, 1, labelVar = TRUE)
```


### **Tune Mtry **
```{r}
# Tune mtry
t <- tuneRF(training[,-31], training[,31],
       stepFactor = 0.5,
       plot = TRUE,
       ntreeTry = 300,
       trace = FALSE,
       improve = 0.05)
```